Development of Lifelong SLAM System for Service Robots using Deep Semantic Information

点击翻译按钮获取中文摘要

This study aims to develop a lifelong semantic navigation system for mobile robots in dynamic environments by addressing two main problems, 1. Semantic understanding of everyday objects in indoor environments for long-term mapping and navigation, and 2. Planning under uncertainty for object search, detection, and exploration using multi-layered semantic maps. Data collection in simulated and real environments and tests for search-based optimized planning has been conducted. The project aims to enhance human-robot interaction in service robots by providing deep understanding of daily objects and their associations with locations, and developing an ontology for lifelong learning. This system can improve performance in tasks like search and exploration in dynamic settings.